This is an implementation of cluster resolvers for Kubernetes. When given the
the Kubernetes namespace and label selector for pods, we will retrieve the
pod IP addresses of all running pods matching the selector, and return a
ClusterSpec based on that information.

Args

job_to_label_mapping

A mapping of TensorFlow jobs to label selectors.
This allows users to specify many TensorFlow jobs in one Cluster
Resolver, and each job can have pods belong with different label
selectors. For example, a sample mapping might be

(Optional) The RPC layer TensorFlow should use to communicate
between tasks in Kubernetes. Defaults to 'grpc'.

override_client

The Kubernetes client (usually automatically retrieved
using from kubernetes import client as k8sclient). If you pass this
in, you are responsible for setting Kubernetes credentials manually.

Raises

ImportError

If the Kubernetes Python client is not installed and no
override_client is passed in.

RuntimeError

If autoresolve_task is not a boolean or a callable.

Attributes

environment

Returns the current environment which TensorFlow is running in.

There are two possible return values, "google" (when TensorFlow is running
in a Google-internal environment) or an empty string (when TensorFlow is
running elsewhere).

If you are implementing a ClusterResolver that works in both the Google
environment and the open-source world (for instance, a TPU ClusterResolver
or similar), you will have to return the appropriate string depending on the
environment, which you will have to detect.

Otherwise, if you are implementing a ClusterResolver that will only work
in open-source TensorFlow, you do not need to implement this property.

master

You must have set the task_type and task_id object properties before
calling this function, or pass in the task_type and task_id
parameters when using this function. If you do both, the function parameters
will override the object properties.

num_accelerators

This returns the number of accelerator cores (such as GPUs and TPUs)
available per worker.

Optionally, we allow callers to specify the task_type, and task_id, for
if they want to target a specific TensorFlow process to query
the number of accelerators. This is to support heterogenous environments,
where the number of accelerators cores per host is different.

Args

task_type

(Optional) The type of the TensorFlow task of the machine we
want to query.

task_id

(Optional) The index of the TensorFlow task of the machine we
want to query.

config_proto

(Optional) Configuration for starting a new session to
query how many accelerator cores it has.